What is a Simulation?
In process industries, simulation software is traditionally used to obtain insights on the process or asset without having to do experiments. Simulations are typically used in process design and optimization to predict what may happen in the real world and run ‘what-if’ scenarios.
Why ML-leveraged simulation twins?
Detailed simulations which capture the process reasonably are generally computationally-intensive and hence time-consuming, sometimes taking hours to days to give results. This has been hampering the adoption of these tools, like CFD and DEM. AI/ML techniques can be applied on simulation data to provide the practitioner on-the-fly detailed-level insights obtained from the simulation. This enables the practitioner to play out extensive ‘what-if’ scenarios to gain detailed process understanding and to leverage such insights for real-time decision-making.
Let us look at a couple of scenarios.
Use Case (I): ‘Forward’ model
Here we present a case where the developed ML-twin can predict the outcome of a simulation by taking in the inputs which go into the simulation model.
Below is an example of DEM simulation of Powder Flowability test , where a bulk powder property Static Angle of Repose (SAoR) is obtained as an outcome of the simulation as shown in Figure 3. Simulations can be run over a parametric space of inputs (like coefficient of static and rolling friction, Young’s Modulus, particle size, particle cohesiveness) to generate data for the ML-model. Here, we use data of 53 simulations reported in the literature .